Robust rate adaptation for 802.11 wireless networks

TLDR

Rate adaptation, an unspecified 802.11 mechanism, is essential for exploiting multi‑rate physical layer capabilities but must accurately estimate channel conditions amid fading, mobility, and hidden terminals. The study aims to critique prevailing design guidelines for rate‑adaptation algorithms and to develop a new Robust Rate Adaptation Algorithm (RRAA) that addresses these challenges. RRAA employs short‑term loss ratio to opportunistically adjust rates and an adaptive RTS filter to avoid collision‑induced rate drops, building on a critique of five common design guidelines. The authors find that existing guidelines can mislead and that RRAA outperforms ARF, AARF, and SampleRate in all tested scenarios, achieving throughput gains up to 143 %.

Abstract

Rate adaptation is a mechanism unspecified by the 802.11 standards, yet critical to the system performance by exploiting the multi-rate capability at the physical layer.I n this paper, we conduct a systematic and experimental study on rate adaptation over 802.11 wireless networks. Our main contributions are two-fold. First, we critique five design guidelines adopted by most existing algorithms. Our study reveals that these seemingly correct guidelines can be misleading in practice, thus incur significant performance penalty in certain scenarios. The fundamental challenge is that rate adaptation must accurately estimate the channel condition despite the presence of various dynamics caused by fading, mobility and hidden terminals. Second, we design and implement a new Robust Rate Adaptation Algorithm (RRAA)that addresses the above challenge. RRAA uses short-term loss ratio to opportunistically guide its rate change decisions, and an adaptive RTS filter to prevent collision losses from triggering rate decrease. Our extensive experiments have shown that RRAA outperforms three well-known rate adaptation solutions (ARF, AARF, and SampleRate) in all tested scenarios, with throughput improvement up to 143%.